2024-09-24 16:39:45 +02:00

93 lines
2.5 KiB
Python
Executable File

#!/usr/bin/env python3
#
# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import lc3
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
matplotlib.use('QtAgg')
parser = argparse.ArgumentParser(description='LC3 Encoder')
parser.add_argument(
'--frame_duration', help='Frame duration (ms)', type=float, default=10)
parser.add_argument(
'--sample_rate', help='Sampling frequency (Hz)', type=float, default=48000)
parser.add_argument(
'--hrmode', help='Enable high-resolution mode', action='store_true')
parser.add_argument(
'--bitrate', help='Bitrate (bps)', type=int, default=96000)
parser.add_argument(
'--libpath', help='LC3 Library path')
args = parser.parse_args()
# --- Setup encoder + decoder ---
fs = args.sample_rate
enc = lc3.Encoder(
args.frame_duration, fs, hrmode=args.hrmode, libpath=args.libpath)
dec = lc3.Decoder(
args.frame_duration, fs, hrmode=args.hrmode, libpath=args.libpath)
frame_len = enc.get_frame_samples()
delay_len = enc.get_delay_samples()
# --- Generate 10 seconds chirp ---
n = 10 * int(fs)
t = np.arange(n - (n % frame_len)) / fs
s = signal.chirp(t, f0=10, f1=fs/2, t1=t[-1], phi=-90, method='linear')
# --- Encoding / decoding loop ---
frame_size = enc.get_frame_bytes(args.bitrate)
bitrate = enc.resolve_bitrate(frame_size)
y = np.empty(len(s) + frame_len)
for i in range(0, len(s), frame_len):
y[i:i+frame_len] = dec.decode(enc.encode(s[i:i+frame_len], frame_size))
y[len(s):] = dec.decode(enc.encode(np.zeros(frame_len), frame_size))
y = y[delay_len:len(s)+delay_len]
# --- Plot spectrograms ---
fig, (ax1, ax2) = plt.subplots(nrows=2, sharex=True)
NFFT = 512
for (ax, s) in [(ax1, s), (ax2, y)]:
ax.specgram(s, Fs=fs, NFFT=NFFT, pad_to=4*NFFT, noverlap=NFFT//2,
vmin=-160, vmax=0)
ax1.set_title('Input signal')
ax1.set_ylabel('Frequency (Hz)')
ax2.set_title(('Processed signal (%.1f kbps)' % (bitrate/1000)))
ax2.set_ylabel('Frequency (Hz)')
plt.show()